Autonomous Task Completion Based on Goal-directed Answer Set Programming

TL;DR

This paper introduces a novel approach combining inductive learning with goal-directed answer set programming to improve explainability and reliability in autonomous task planning, demonstrated through a Python-based implementation using s(CASP).

Contribution

It presents an innovative integration of inductive learning with answer set programming for autonomous task planning, enhancing explainability and reliability.

Findings

Developed a Python harness utilizing s(CASP) for task solving.

Achieved computational efficiency in solving task problems.

Exploring solutions for complex simulated task completion.

Abstract

Task planning for autonomous agents has typically been done using deep learning models and simulation-based reinforcement learning. This research proposes combining inductive learning techniques with goal-directed answer set programming to increase the explainability and reliability of systems for task breakdown and completion. Preliminary research has led to the creation of a Python harness that utilizes s(CASP) to solve task problems in a computationally efficient way. Although this research is in the early stages, we are exploring solutions to complex problems in simulated task completion.